There is no assay currently capable of accurately measuring the true size of the functional, replication- competent component of the HIV latent reservoir. Moreover, none of the existing approaches to detect latent infection allow for the ex vivo capture and enrichment of viable latently infected cells from HIV-infected patients on combination antiretroviral therapy (cART). This necessitates reliance on the use of in vitro model cell systems both to study the mechanisms underlying viral latency and to explore therapies to target latent infection. The objective of this application is to develop a protein-basd method to selectively identify latently infected cells that will be amenable to practical flow cytometric-based approaches for direct quantification and isolation of cells from the latent reservoir in vivo. The central hypothesis is that protein biomarkers of latently infected cells exit and can be used for their specific detection. The rationale of the proposed research is that identification of a unique protein signature will allow quantification of latently infected cells, without the requirement for virus reactivation, and will provide a means to isolate viable latently infected cells from HIV- infected patients. We will test our hypothesis by pursuing the following specific aims: 1) Identify protein biomarkers associated with latent HIV infection of primary CD4 T cells; 2) Use commercially available antibody reagents to a subset of the identified protein biomarkers to assess the ability to enrich for latently infected cells from a mixed cell population 3) Develop an optimized monoclonal antibody panel for sensitive and specific detection of latently infected primary CD4 T cells; 4) Determine the ability of the developed antibody panel(s) to accurately identify latently infected cells in PBMC from HIV-infected patients. In the R21 phase, Aim 1 will utilize two in vitro primary T cell models and a novel, highly sensitive quantitative proteomics (iTRAQ) technology to interrogate the entire proteome of latently infected and uninfected CD4 T cells. Biomarkers of latency will be identified using the limma modeling package (Bioconductor R) and correlation analysis of protein expression with the percentage of latently infected cells in each sample. Aim 2 will provide proof of principle by testing commercially available antibodies, against a small group of identified plasma membrane proteins, to demonstrate enrichment of latently infected cells from a cell mixture. In the R33 phase, Aim 3 will optimize the antibody panel for sensitive and specific detection of latently infected cells, using three latency models (Spina, Karn and Greene). In Aim 4, the antibody panel(s) with the optimal and most consistent performance across the primary T cell models will be evaluated, using PBMC from HIV-infected patients on suppressive cART. Our approach is direct, but innovative; it will identify host, not viral, determinants that are associated selectivly with latent infection. The research is significant, because it will develop a quick, practical method to specifically detect latently infected cells, without virus reactivation. Moreover, the identified protein biomarkers may serve as therapeutic targets in future efforts to selectively destroy cells of the latent reservoir.